Associating shoppers together

ABSTRACT

A store having an automated checkout can automatically associate multiple shoppers with a single purchase. For example, a mother and her children can walk independently through the store and select items. Upon checkout, the items selected by the mother and children can all be charged to the mother&#39;s credit card in a single purchase. The system and method discussed herein can capture images of multiple shoppers from one or more video streams of a store area, can associate the multiple shoppers with one another from the captured images when the multiple shoppers appear together in a check-in area of the store, can track the movement of the multiple shoppers within the store from the captured images, can track items selected by the multiple shoppers from the captured images, and can initiate a transaction that charges the selected items to an account associated with one of the multiple shoppers.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to automatically trackingshoppers in a store having an automated checkout.

BACKGROUND OF THE DISCLOSURE

In a store having an automated checkout, a single purchase may beassociated with multiple shoppers. For example, a mother and her twochildren may produce a single order, rather than three separate orders,at the store checkout. For a store having an automated checkout, itwould be beneficial to automatically associate the multiple shopperswith the single purchase.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of a video surveillance system that can trackthe movement of people within a confined area, such as a store, inaccordance with some examples.

FIG. 2 shows a flowchart of an example of a method for conducting aretail transaction for a store that includes a video surveillance systemand frictionless checkout, in accordance with some examples.

FIG. 3 shows a flowchart of an example of a method for conducting aretail transaction for a store that includes a video surveillance systemand frictionless checkout, in accordance with some examples.

FIG. 4 shows a block diagram of an example of a controller, inaccordance with some examples.

Corresponding reference characters indicate corresponding partsthroughout the several views. Elements in the drawings are notnecessarily drawn to scale. The configurations shown in the drawings aremerely examples, and should not be construed as limiting the scope ofthe inventive subject matter in any manner.

DETAILED DESCRIPTION

A store having video surveillance and an automated (e.g., frictionless)checkout can automatically associate multiple shoppers with a singlepurchase.

The system and method discussed herein can capture images of multipleshoppers from one or more video streams of a store area, canautomatically associate the multiple shoppers with one another from thecaptured images when the multiple shoppers appear together in a check-inarea of the store, can track the movement of the multiple shopperswithin the store from the captured images, can track items selected bythe multiple shoppers from the captured images, and can initiate atransaction that charges the selected items to an account associatedwith one of the multiple shoppers.

For example, a mother and her children can walk independently through astore and select items. At an automated (e.g., frictionless) checkout ofthe store, the items selected by the mother and children can all becharged to the mother's credit card in a single purchase.

Performing this automatic association of multiple shoppers with oneanother can leverage use of a store's tracking system, and can simplifycheckout by not requiring explicit input from the multiple shoppers todetermine if their selected items are to be combined into a singlepurchase.

The tracking system is first described below, after which specificaspects of the tracking system are described that allow the trackingsystem to automatically associate multiple shoppers together for asingle purchase.

In the store's tracking system, one or more cameras can monitor ashopping area of the store. Each video frame captured by the camera orcameras can be sent to a convolutional neural network deep learningmodel that does single shot object detection for people. In someexamples, the rate at which frames are sent may not correspond to theframe rate of the video camera. In some examples, the rate at whichframes are sent can be 15 or fewer frames per second, 10 or fewer framesper second, 5 or fewer frames per second, one frame per second, oranother suitable value. The object detection can return back all peoplethat are present in the image. The images of the people can then becropped and sent to an alternate convolutional neural network that cancompute the graham matrix to figure out the style of the person'sclothes. The system can use techniques like optical flow, kalmanfilters, and deep KNN to track the individuals and log theirgeographical location in the store. Analytics can be easily computed onthis data to answer questions like: where are the current storeemployees, what areas of the store need more store clerks, where doshoppers spend most of their time, what areas of the store are mostpopular, what areas of the store are least popular, which storeemployees are not where they are supposed to be, which store employeesare not present, how many total shoppers are in the store, and so forth.The system can perform all of these analytics and log them in real time.

The system can also identify store items that are handled by theshoppers. When a shopper handles an item, the system can add the item toa virtual cart, which includes items to be purchased by the shopper.When the shopper returns an item to a shelf, the item can be removedfrom the virtual cart. Keeping track of items in this manner can allowshoppers to store their items to be purchased in a cart without havingto ensure that the items are visible in the cart, and can allow shoppersto check out without having to remove the items from the cart, such asby placing them on a counter or conveyor to be scanned.

One technique that has been developed to perform tracking is known as“deep SORT”. Deep SORT improves upon a known “SORT” technique byincorporating appearance information through a pre-trained associationmetric, which allows deep SORT to perform tracking through longerperiods of occlusion.

The system and method discussed herein can capture images from one ormore video streams of a store area, and can use deep learning to trackmovement of the people within the store. The tracked movement canprovide information that is useful to operators of the store, such aswhere shoppers and store employees are, what items have been selected orhandled by shoppers, how long the shoppers and/or employees have been incertain areas of the store, which areas of the store need moreemployees, where most shoppers are concentrated within the store, whichareas of the store are popular, and so forth. The system and method canprovide information for checking out a shopper, including which itemshave been selected for purchase by the shopper. The system and methodcan also log movement information, for downstream use.

As an off-the-shelf system, deep SORT is found to be excessivelycomplicated and ill suited for the task of tracking employees andshoppers in a store. The system and method discussed herein includemodifications to the deep SORT technique, which result in a techniquethat is well suited to the task of tracking shoppers, and the items tobe purchased, in a store.

When a group of shoppers, such as a mother and her children, enter astore, the group of shoppers can check in at a designated check-inlocation in the store. One particular shopper in the group of shopperscan check in at the designated check-in location. The particular shoppercan identify himself or herself at the designated check-in location,such as by scanning an optical identifier (e.g., a bar code or a QR codeprinted on a card or displayed on a screen of a mobile device), scanninga radiofrequency identifier (such as a card that includes aradiofrequency identification tag), scanning a credit card or driver'slicense, scanning a biometric identifier, such as a fingerprint or aretina, or other suitable identification. Once the store'sidentification system has obtained the shopper's identity, the systemcan link the identity to a payment method associated with the identity,such as a credit card number, a bank account number, an electronicpayment identifier (such as a PayPal account email address), or others.If the system does not have a payment method already associated with theshopper's identity, the check-in system can prompt the shopper to entera payment method.

While the group of shoppers is located at the check-in location in thestore, the store's tracking system can capture one or more images of theshoppers, and can assign a common identifier to each shopper in thegroup of shoppers. When the tracking system recognizes the shoppers insubsequent images from the video stream, the tracking system canassociate store items handled or selected by any of the shoppers in thegroup with the common identifier, so that all the items may be purchasedin a single transaction at checkout.

In a specific example, a group of shoppers can enter a designatedcheck-in area of a store. A video processing system can capture imagesof group of shoppers while the shoppers are in the check-in area, and,based on proximity, decide that the shoppers should be grouped together.The system can then assign the shoppers in the group a uniqueidentifier. The system can form cropped images of the bodies of theidentified shoppers in the group, and send the cropped images to adeep-learning autoencoder. The autoencoder can return a 128-dimensionalfeature vector that is stored in a K-Nearest Neighbors database. Asmembers of the group of shoppers pick up and put down items around thestore area, the system can send image crops of their bodies are sent tothe autoencoder, which can return a feature vector that is queriedagainst the K-Nearest Neighbors database. The system can search for theidentifier value of the nearest feature vector based on the euclideandistance metric, which can be the identifier for the group.

In some examples, the system can leverage the tracking algorithm totrack shoppers, and groups of shoppers, throughout the store. To link agroup of shoppers to a common cart, the full group can enter thecheck-in area at the same time. The system can detect each member of thegroup, extract each person's feature vector, and store the featurevectors in a suitable lookup table. The system can keep note of theshopper identifiers who were in the check-in area together and map themto the same cart identifier.

FIG. 1 shows an example of a video surveillance system 100 that cantrack the movement of people 102A-B within a confined area 104, such asa store, in accordance with some examples. The configuration of FIG. 1is but one example; other suitable configurations can also be used.

A video camera 106 can be positioned to capture a video stream 108 of aconfined area 104, such as the shopping area of a store. In someexamples, an optional second video camera 110 can be positioned tocapture an optional second video stream 112 of at least a portion of theconfined area 104. In some examples, at least one of the video cameras106, 110 can capture at least a portion of a designated check-in area124 of the store, and a designated check-out area 126 of the store. Insome examples, the fields of view 118, 120 of the cameras can overlap inat least a portion of the confined area 104. In other examples, thefields of view may not overlap. In some examples, the video streams fromthe cameras can be processed downstream in a manner such that thecameras need not be explicitly aligned or registered to each other, suchas by providing spatial (x, y, z) coordinates of the cameras.Eliminating the alignment of the cameras to each other is a benefit ofthe manner in which the video streams are processed. In some examples,there can be more than two cameras, each producing a corresponding videostream of at least a portion of the confined area 104.

A video interface 114 can receive the video stream 108, the optionalsecond video stream 112, and any optional additional streams fromoptional additional cameras. In some examples, the video interface 114can be a stand-alone piece of hardware, coupled to additional processorsand networks as needed. In other examples, the video interface caninclude one or more dedicated cards in a computer or server. In stillother examples, the video interface can be realized entirely in softwareand coupled to the processor, discussed below.

A processor 116 can be coupled to the video interface 114. The processor116 can include one or more processors in a machine running locally(e.g., in the store), and/or one or more processors in a server at aremote location and connected to a server in the store through suitablenetwork connections. The processor 116 can execute computinginstructions to perform data processing activities. The data processingactivities can include operations that pertain to processing of thevideo stream 108, the optional second video stream 112, and any optionaladditional streams from optional additional cameras. Such operations areexplained in the context of FIG. 2.

In some examples the processor 116 can automatically generate a singlepurchase 122 at the checkout 126, for items flagged for purchase byshoppers in the group of shoppers 102A-B. In some examples, uponcompletion of the single purchase 122, the processor can optionallygenerate a paper receipt 128 for the shoppers 102A-B or send anelectronic receipt 128 to the shopper whose credit card was charged. Insome examples, the system can specify that two or more shoppers aresplitting the cost of the purchase, optionally splitting evenly, and cangenerate the suitable number of purchases to respective accounts atcheckout. This is but one example of instructions; other suitableinstructions can also be used.

FIG. 2 shows a flowchart of an example of a method 200 for conducting aretail transaction for a store that includes a video surveillance systemand frictionless checkout, in accordance with some examples. The method200 of FIG. 2 can be executed by the system 100 of FIG. 1, or any othersuitable system. The method 200 of FIG. 2 is but one example of a methodfor conducting a retail transaction for a store that includes a videosurveillance system and frictionless checkout; other suitable methodscan also be used. As explained above, the processor coupled to the videointerface can execute computing instructions to perform data processingactivities. The data processing activities can include the operationsshown in FIG. 2 and discussed below.

At operation 202, at a check-in location of a store, a selection can bereceived of an account to be charged for a purchase. The account can beassociated with a shopper. In some examples, receiving the selection ofthe account can include: receiving input of data corresponding to acredit card number from the shopper. In some examples, receiving thedata corresponding to the credit card number can include: receiving acredit card swipe. In some examples, receiving the selection of theaccount can include: reading a radiofrequency identification tag thatcorresponds to the shopper; and retrieving, from a server, a storedcredit number that corresponds to the shopper. In some examples,receiving the selection of the account can include: scanning a visualcode, such as a QR code printed on a card or displayed on a screen, thatcorresponds to the shopper; and retrieving, from a server, a storedcredit number that corresponds to the shopper. In some examples,receiving the selection of the account can include: scanning a biometricindicator, such as a fingerprint scan, a retina, or a facial scan, thatcorresponds to the shopper; and retrieving, from a server, a storedcredit number that corresponds to the shopper.

At operation 204, video surveillance of the store can identify theshopper as being one of a group of shoppers that are present in thecheck-in location of the store when the selection is received. In someexamples, identifying the shopper as being one of the group of shopperscan include: receiving at least one image from the video surveillancesystem, the at least one image including the check-in location of thestore; and assigning a common cart identifier to each person identifiedin the at least one image, the common cart identifier identifying allthe shoppers in the group. In some examples, identifying the shopper asbeing one of the group of shoppers can include: forming cropped imagesof bodies of the identified shoppers in the group; sending the croppedimages to at least one processor configured to execute a deep-learningautoencoder; and storing, on a storage device coupled to the at leastone processor, a multi-dimensional feature vector in a nearest-neighborsdatabase.

At operation 206, the video surveillance of the store can track movementof the group of shoppers throughout the store. In addition, the videosurveillance of the store can track items in the store selected forpurchase by any of the shoppers in the group of shoppers. In someexamples, the video surveillance can track the items in the store bytracking items that are handled by any of the group of shoppers. In someexamples, tracking the items in the store selected for purchase by anyof the shoppers in the group of shoppers can include: sensing, with thevideo surveillance of the store, that a first shopper, of the group ofshoppers, has picked up a first item from a shelf or rack; and addingthe first item to a virtual shopping cart. In some examples, trackingthe items in the store selected for purchase by any of the shoppers inthe group of shoppers can further include: sensing, with the videosurveillance of the store, that the first shopper has returned the firstitem to the shelf or rack; and removing the first item from the virtualshopping cart. In some examples, tracking the items in the storeselected for purchase can include: sensing, with the video surveillanceof the store, that a first item has been picked up; sending, to thedeep-learning autoencoder, a cropped image of the person who picked upthe first item; retrieving, from the deep-learning autoencoder, afeature vector corresponding to the sent cropped image; calculating aEuclidean distance metric between the retrieved feature vector andfeature vectors stored on the storage device; and identifying, from thestored feature vector having the shortest Euclidean distance to theretrieved feature vector, a first shopper as the person who picked upthe first item.

At operation 208, a frictionless check-out of the store can charge theselected account for purchase of the selected items. In some examples,the shoppers can leave the purchasable items in a shopping cart, withouthaving to place them on a conveyor for scanning. In some examples, theshoppers can wheel the shopping cart through a designated check-out areaof the store, the video surveillance of the store can recognize theshoppers in the check-out area, can check to see which accounts areassociated with the shoppers (in this case, the group of shoppers areall associated with a single account), and can charge the account forpurchase of the items in the shopping cart (which have all been selectedfor purchase as the shoppers moved through the store area).

FIG. 3 shows a flowchart of an example of a method 300 for conducting aretail transaction for a store that includes a video surveillance systemand frictionless checkout, in accordance with some examples. The method300 of FIG. 3 can be executed by the system 100 of FIG. 1, or any othersuitable system. The method 300 of FIG. 3 is but another example of amethod for conducting a retail transaction for a store that includes avideo surveillance system and frictionless checkout; other suitablemethods can also be used. As explained above, the processor coupled tothe video interface can execute computing instructions to perform dataprocessing activities. The data processing activities can include theoperations shown in FIG. 3 and discussed below.

At operation 302, at a check-in location of a store, a user interfacecan receive a selection of an account to be charged for a purchase. Theuser interface can be a kiosk or terminal in the check-in location ofthe store. The account, such as a credit card number, a bank routing andaccount number, or an electronic payment account, can be associated witha shopper.

At operation 304, a stored credit number that corresponds to the accountcan be retrieved from a server.

At operation 306, video surveillance of the store can identify theshopper as being one of a group of shoppers that are present in thecheck-in location of the store when the selection is received.

At operation 308, the video surveillance of the store can track movementof the group of shoppers throughout the store and items in the storeselected for purchase by any of the shoppers in the group of shoppers.

At operation 310, through a frictionless check-out of the store, theretrieved credit card number can be charged for purchase of the selecteditems.

FIG. 4 shows a block diagram of an example of a controller 400, inaccordance with some examples. The controller 400 can be part of asystem that can track people in a confined area, such as a store. Theexample of FIG. 4 is but one configuration for a controller; otherconfigurations can also be used.

In one example, multiple such controllers 400 are utilized in adistributed network to implement multiple components in a transactionbased environment. An object-oriented, service-oriented, or otherarchitecture may be used to implement such functions and communicatebetween the multiple controllers 400 and components.

One example of a controller 400, in the form of a computer 410, caninclude a processing unit 402, memory 404, removable storage 412, andnon-removable storage 414. Memory 404 may include volatile memory 406and non-volatile memory 408. Computer 410 may include, or have access toa computing environment that includes, a variety of computer-readablemedia, such as volatile memory 406 and non-volatile memory 408,removable storage 412 and non-removable storage 414. Computer storageincludes random access memory (RAM), read only memory (ROM), erasableprogrammable read-only memory (EPROM) and electrically erasableprogrammable read-only memory (EEPROM), flash memory or other memorytechnologies, compact disc read-only memory (CD-ROM), Digital VersatileDisks (DVD) or other optical disk storage, magnetic cassettes, magnetictape, magnetic disk storage or other magnetic storage devices, or anyother medium capable of storing computer-readable instructions. Computer410 may include or have access to a computing environment that includesinput 416, output 418, and a communication connection 420. The input 416can include a video interface. The computer may operate in a networkedenvironment using a communication connection to connect to one or moreremote computers, such as database servers. The remote computer mayinclude a personal computer (PC), server, router, network PC, a peerdevice or other common network node, or the like. The communicationconnection may include a Local Area Network (LAN), a Wide Area Network(WAN) or other networks.

Computer-readable instructions stored on a computer-readable medium areexecutable by the processing unit 402 of the computer 410. A hard drive,CD-ROM, and RAM are some examples of articles including a non-transitorycomputer-readable medium. For example, a computer program 422 withinstructions for the computer 410, according to the teachings of thepresent disclosure, may be included on a CD-ROM and loaded from theCD-ROM to a hard drive. The computer-readable instructions allowcomputer 410 to provide generic access controls in a COM based computernetwork system having multiple users and servers.

In the foregoing detailed description, the method and apparatus of thepresent disclosure have been described with reference to specificembodiments thereof. It will, however, be evident that variousmodifications and changes may be made thereto without departing from thebroader spirit and scope of the present disclosure. The presentspecification and figures are accordingly to be regarded as illustrativerather than restrictive.

To further illustrate the device and related method disclosed herein, anon-limiting list of examples is provided below. Each of the followingnon-limiting examples can stand on its own, or can be combined in anypermutation or combination with any one or more of the other examples.

In Example 1, a method can include: receiving, at a check-in location ofa store, selection of an account to be charged for a purchase, theaccount being associated with a shopper; identifying, with videosurveillance of the store, the shopper as being one of a group ofshoppers that are present in the check-in location of the store when theselection is received; tracking, with the video surveillance of thestore, movement of the group of shoppers throughout the store and itemsin the store selected for purchase by any of the shoppers in the groupof shoppers; and charging, through a frictionless check-out of thestore, the selected account for purchase of the selected items.

In Example 2, the method of Example 1 can optionally be configured suchthat the video surveillance tracks the items in the store by trackingitems that are handled by any of the group of shoppers.

In Example 3, the method of any one of Examples 1-2 can optionally beconfigured such that tracking the items in the store selected forpurchase by any of the shoppers in the group of shoppers comprises:sensing, with the video surveillance of the store, that a first shopper,of the group of shoppers, has picked up a first item from a shelf orrack; and adding the first item to a virtual shopping cart.

In Example 4, the method of any one of Examples 1-3 can optionally beconfigured such that tracking the items in the store selected forpurchase by any of the shoppers in the group of shoppers furthercomprises: sensing, with the video surveillance of the store, that thefirst shopper has returned the first item to the shelf or rack; andremoving the first item from the virtual shopping cart.

In Example 5, the method of any one of Examples 1-4 can optionally beconfigured such that receiving the selection of the account comprises:receiving input of data corresponding to a credit card number from theshopper.

In Example 6, the method of any one of Examples 1-5 can optionally beconfigured such that receiving the data corresponding to the credit cardnumber comprises: receiving a credit card swipe.

In Example 7, the method of any one of Examples 1-6 can optionally beconfigured such that receiving the selection of the account comprises:reading a radiofrequency identification tag that corresponds to theshopper; and retrieving, from a server, a stored credit number thatcorresponds to the shopper.

In Example 8, the method of any one of Examples 1-7 can optionally beconfigured such that receiving the selection of the account comprises:scanning a visual code that corresponds to the shopper; and retrieving,from a server, a stored credit number that corresponds to the shopper.

In Example 9, the method of any one of Examples 1-8 can optionally beconfigured such that receiving the selection of the account comprises:scanning a biometric indicator that corresponds to the shopper; andretrieving, from a server, a stored credit number that corresponds tothe shopper.

In Example 10, the method of any one of Examples 1-9 can optionally beconfigured such that identifying the shopper as being one of the groupof shoppers comprises: receiving at least one image from the videosurveillance system, the at least one image including the check-inlocation of the store; and assigning a common cart identifier to eachperson identified in the at least one image, the common cart identifieridentifying all the shoppers in the group.

In Example 11, the method of any one of Examples 1-10 can optionally beconfigured such that identifying the shopper as being one of the groupof shoppers comprises: forming cropped images of bodies of theidentified shoppers in the group; sending the cropped images to at leastone processor configured to execute a deep-learning autoencoder; andstoring, on a storage device coupled to the at least one processor, amulti-dimensional feature vector in a nearest-neighbors database.

In Example 12, the method of any one of Examples 1-11 can optionally beconfigured such that tracking the items in the store selected forpurchase comprises: sensing, with the video surveillance of the store,that a first item has been picked up; sending, to the deep-learningautoencoder, a cropped image of the person who picked up the first item;retrieving, from the deep-learning autoencoder, a feature vectorcorresponding to the sent cropped image; calculating a Euclideandistance metric between the retrieved feature vector and feature vectorsstored on the storage device; and identifying, from the stored featurevector having the shortest Euclidean distance to the retrieved featurevector, a first shopper as the person who picked up the first item.

In Example 13, a system can include: at least one video camerapositioned to capture at least one video stream of a store; a videointerface configured to receive the at least one video stream; and aprocessor coupled to the video interface and configured to executecomputing instructions to perform data processing activities, the dataprocessing activities comprising: receiving, at a check-in location ofthe store, selection of an account to be charged for a purchase, theaccount being associated with a shopper; identifying, from the at leastone video stream, the shopper as being one of a group of shoppers thatare present in the check-in location of the store when the selection isreceived; tracking, from the at least one video stream, movement of thegroup of shoppers throughout the store and items in the store selectedfor purchase by any of the shoppers in the group of shoppers; andcharging, through a frictionless check-out of the store, the selectedaccount for purchase of the selected items.

In Example 14, the system of Example 13 can optionally be configuredsuch that tracking the items in the store comprises tracking items thatare handled by any of the group of shoppers.

In Example 15, the system of any one of Examples 13-14 can optionally beconfigured such that tracking the items in the store comprises: sensing,from the at least one video stream, that a first shopper, of the groupof shoppers, has picked up a first item from a shelf or rack; and addingthe first item to a virtual shopping cart.

In Example 16, the system of any one of Examples 13-15 can optionally beconfigured such that tracking the items in the store further comprises:sensing, from the at least one video stream, that the first shopper hasreturned the first item to the shelf or rack; and removing the firstitem from the virtual shopping cart.

In Example 17, the system of any one of Examples 13-16 can optionally beconfigured such that receiving the selection of the account comprises:receiving input of data corresponding to a credit card number from theshopper.

In Example 18, the system of any one of Examples 13-17 can optionally beconfigured such that identifying the shopper as being one of the groupof shoppers comprises: receiving at least one image from the at leastone video stream, the at least one image including the check-in locationof the store; and assigning a common cart identifier to each personidentified in the at least one image, the common cart identifieridentifying all the shoppers in the group.

In Example 19, the system of any one of Examples 13-18 can optionally beconfigured such that identifying the shopper as being one of the groupof shoppers comprises: forming cropped images of bodies of theidentified shoppers in the group; sending the cropped images to adeep-learning autoencoder configured to execute on the processor; andstoring, on a storage device coupled to the processor, amulti-dimensional feature vector in a nearest-neighbors database.

In Example 20, a method can include: receiving, at a check-in locationof a store, selection of an account to be charged for a purchase, theaccount being associated with a shopper; retrieving, from a server, astored credit number that corresponds to the account; identifying, withvideo surveillance of the store, the shopper as being one of a group ofshoppers that are present in the check-in location of the store when theselection is received; tracking, with the video surveillance of thestore, movement of the group of shoppers throughout the store and itemsin the store selected for purchase by any of the shoppers in the groupof shoppers; and charging, through a frictionless check-out of thestore, the retrieved credit card number for purchase of the selecteditems.

What is claimed is:
 1. A method, comprising: receiving, at a check-inlocation of a store, selection of a first account to be charged for apurchase, the first account being associated with a first shopper;receiving, at the check-in-location of the store, selection of a secondaccount to be charged for the purchase, the second account beingassociated with a second shopper; identifying, with video surveillancesystem of the store, the first shopper and the second shopper as beingone of a group of shoppers that are present in the check-in location ofthe store when the selection is received; assigning a personalidentifier to each member of the group of shoppers, wherein the personalidentifier is unique to each member of the group of shoppers, andwherein the personal identifier is based on a feature vector of a firstshopper, wherein the first shopper is a particular one of each member ofthe group of shoppers to which the personal identifier is assigned,wherein the feature vector is a multi-dimensional feature vector createdfrom a first image using a deep-learning autoencoder, and wherein thefirst image includes at least the first shopper; assigning a common cartidentifier to each member of the group of shoppers, the common cartidentifier identifying all the shoppers in the group; tying the commoncart identifier with the personal identifier for each member of thegroup of shoppers; tracking based the common cart identifier and on thepersonal identifier for each member of the group of shoppers, with thevideo surveillance of the store, movement of each member of the group ofshoppers throughout the store and items in the store selected forpurchase by any of the shoppers in the group of shoppers, wherein thetracking includes sensing when the first shopper or the second shopperremoves a first item from a shelf or rack, wherein the first item is aparticular one of the items in the store, wherein the sensing is made bysending a second image to the deep-learning autoencoder, retrieving asecond feature vector from the second image, and comparing the secondfeature vector with the feature vector, and wherein the second imageincludes at least the first shopper; specifying that a cost of thepurchase of the selected items is to be split between the first shopperand the second shopper; charging, through a frictionless check-out ofthe store, the first account for a first portion of the cost of thepurchase to be split between the first shopper and the second shopperfor the purchase of the selected items; and charging, through thefrictionless check-out of the store, the second account for a secondportion of the cost of the purchase to be split between the firstshopper and the second shopper for the purchase of the selected items.2. The method of claim 1, wherein the video surveillance tracks theitems in the store by tracking items that are handled by any of thegroup of shoppers.
 3. The method of claim 2, wherein tracking the itemsin the store selected for purchase by any of the shoppers in the groupof shoppers comprises: adding the first item to a virtual shopping cart.4. The method of claim 3, wherein tracking the items in the storeselected for purchase by any of the shoppers in the group of shoppersfurther comprises: sensing, with the video surveillance of the store,that the first shopper has returned the first item to the shelf or rack;and removing the first item from the virtual shopping cart.
 5. Themethod of claim 1, wherein receiving the selection of the accountcomprises: receiving input of data corresponding to a credit card numberfrom the shopper.
 6. The method of claim 5, wherein receiving the datacorresponding to the credit card number comprises: receiving a creditcard swipe.
 7. The method of claim 1, wherein receiving the selection ofthe account comprises: reading a radiofrequency identification tag thatcorresponds to the shopper; and retrieving, from a server, a storedcredit number that corresponds to the shopper.
 8. The method of claim 1,wherein receiving the selection of the account comprises: scanning avisual code that corresponds to the shopper; and retrieving, from aserver, a stored credit number that corresponds to the shopper.
 9. Themethod of claim 1, wherein receiving the selection of the accountcomprises: scanning a biometric indicator that corresponds to theshopper; and retrieving, from a server, a stored credit number thatcorresponds to the shopper.
 10. The method of claim 1, whereinidentifying the shopper as being one of the group of shoppers comprises:receiving at least one image from the video surveillance system, the atleast one image including the check-in location of the store.
 11. Themethod of claim 1, wherein identifying the shopper as being one of thegroup of shoppers comprises: forming cropped images of bodies of theidentified shoppers in the group; sending the cropped images to at leastone processor configured to execute the deep-learning autoencoder; andstoring, on a storage device coupled to the at least one processor, themulti-dimensional feature vector in a nearest-neighbors database. 12.The method of claim 11, wherein tracking the items in the store selectedfor purchase comprises: sensing, with the video surveillance of thestore, that a first item has been picked up; sending, to thedeep-learning autoencoder, a cropped image of the person who picked upthe first item; retrieving, from the deep-learning autoencoder, afeature vector corresponding to the sent cropped image; calculating aEuclidean distance metric between the retrieved feature vector andfeature vectors stored on the storage device, and identifying, from thestored feature vector having the shortest Euclidean distance to theretrieved feature vector, a first shopper as the person who picked upthe first item.
 13. A system, comprising: at least one video camerapositioned to capture at least one video stream of a store; a videointerface configured to receive the at least one video stream; and aprocessor coupled to the video interface and configured to executecomputing instructions to perform data processing activities, the dataprocessing activities comprising: receiving, at a check-in location ofthe store, selection of a first account to be charged for a purchase,the first account being associated with a first shopper; receiving, atthe check-in-location of the store, selection of a second account to becharged for the purchase, the second account being associated with asecond shopper; identifying, from the at least one video stream, thefirst shopper and the second shopper as being one of a group of shoppersthat are present in the check-in location of the store when theselection is received; assigning a personal identifier to each member ofthe group of shoppers, wherein the personal identifier is unique to eachmember of the group of shoppers, and wherein the personal identifier isbased on a feature vector of a first shopper, wherein the first shopperis a particular one of each member of the group of shoppers to which thepersonal identifier is assigned, wherein the feature vector is amulti-dimensional feature vector created from a first image capturedfrom the at least one video stream using a deep-learning autoencoder,and wherein the first image includes at least the first shopper;assigning a common cart identifier to each member of the group ofshoppers, the common cart identifier identifying all the shoppers in thegroup; tying the common cart identifier with the personal identifier foreach member of the group of shoppers; tracking based on the common cartidentifier and on the personal identifier for each member of the groupof shoppers, from the at least one video stream, movement of each memberof the group of shoppers throughout the store and items in the storeselected for purchase by any of the shoppers in the group of shoppers,wherein the tracking includes sensing when the first shopper or thesecond shopper removes a first item from a shelf or rack, wherein thefirst item is a particular one of the items in the store, and whereinthe sensing is made by sending a second image captured from the at leastone video stream to the deep-learning autoencoder, retrieving a secondfeature vector from the second image, and comparing the second featurevector with the first feature vector, and wherein the second imageincludes at least the first shopper; specifying that a cost of thepurchase of the selected items is to be split between the first shopperand the second shopper; charging, through a frictionless check-out ofthe store, the first account for a first portion of the cost of thepurchase to be split between the first shopper and the second shopperfor the for purchase of the selected items; and charging, through thefrictionless check-out of the store, the second account for a secondportion of the cost of the purchase to be split between the firstshopper and the second shopper for the purchase of the selected items.14. The system of claim 13, wherein tracking the items in the storecomprises tracking items that are handled by any of the group ofshoppers.
 15. The system of claim 14, wherein tracking the items in thestore comprises: adding the first item to a virtual shopping cart. 16.The system of claim 15, wherein tracking the items in the store furthercomprises: sensing, from the at least one video stream, that the firstshopper has returned the first item to the shelf or rack; and removingthe first item from the virtual shopping cart.
 17. The system of claim13, wherein receiving the selection of the account comprises: receivinginput of data corresponding to a credit card number from the shopper.18. The system of claim 13, wherein identifying the shopper as being oneof the group of shoppers comprises: receiving at least one image fromthe at least one video stream, the at least one image including thecheck-in location of the store.
 19. The system of claim 13, whereinidentifying the shopper as being one of the group of shoppers comprises:forming cropped images of bodies of the identified shoppers in thegroup; sending the cropped images to the deep-learning autoencoderconfigured to execute on the processor; and storing, on a storage devicecoupled to the processor, the multi-dimensional feature vector in anearest-neighbors database.
 20. A method, comprising: receiving, at acheck-in location of a store, selection of a first account to be chargedfor a purchase, the first account being associated with a first shopper;receiving, at the check-in-location of the store, selection of a secondaccount to be charged for the purchase, the second account beingassociated with a second shopper retrieving, from a first server, afirst stored credit card number that corresponds to the first account;retrieving, from a second server, a second stored card credit numberthat corresponds to the second account; identifying, with videosurveillance of the store, the first shopper and the second shopper asbeing one of a group of shoppers that are present in the check-inlocation of the store when the selection is received; assigning apersonal identifier to each member of the group of shoppers, wherein thepersonal identifier is unique to each member of the group of shoppers,wherein the personal identifier is based on a feature vector of a firstshopper, wherein the first shopper is a particular one of each member ofthe group of shoppers to which the personal identifier is assigned,wherein the feature vector is a multi-dimensional feature vector createdfrom a first image using a deep-learning autoencoder, and wherein thefirst image includes at least the first shopper; assigning a common cartidentifier to each member of the group of shoppers, the common cartidentifier identifying all the shoppers in the group; tying the commoncart identifier with the personal identifier for each member of thegroup of shoppers; tracking based on the common cart identifier and onthe personal identifier for each member of the group of shoppers, withthe video surveillance of the store, movement of each member of thegroup of shoppers throughout the store and items in the store selectedfor purchase by any of the shoppers in the group of shoppers, whereinthe tracking includes sensing when the first shopper or the secondshopper removes a first item from a shelf or rack, wherein the firstitem is a particular one of the items in the store, wherein the sensingis made by sending a second image to the deep-learning autoencoder,retrieving a second feature vector from the second image, and comparingthe second feature vector with the feature vector, and wherein thesecond image includes at least the first shopper; specifying that a costof the purchase of the selected items is to be split between the firstshopper and the second shopper; charging, through a frictionlesscheck-out of the store, the retrieved first credit card number for afirst portion of the cost of the purchase to be split between the firstshopper and the second shopper for the purchase of the selected itemsand charging, through the frictionless check-out of the store, theretrieved second credit card number for a second portion of the cost ofthe purchase to be split between the first shopper and the secondshopper for the purchase of the selected items.